The present invention relates to character recognition techniques that categorize strings, such as strings of characters or other elements.
Some conventional character recognition techniques improve recognition accuracy by taking into account different categories of character strings that can occur in text. The character recognition products of Xerox Imaging Systems, for example, employ several specialized recognition algorithms, each for a respective category of character string. One algorithm can look up words in a dictionary, another can recognize valid strings of arabic numerals with punctuation marks, and so forth. If a string of character candidates includes a candidate that has a substantial probability for more than one character category, the ambiguity can be resolved by applying each recognition algorithm to the possible string that would result from each probable character category. If one of the possible strings is recognized by one of the algorithms, the recognition result can be used to resolve the ambiguity. If two or more possible strings are recognized by different algorithms, the sequence of categories of strings can be taken into account to resolve the ambiguity.
Takahashi et al., U.S. Pat. No. 4,003,022, describe string pattern recognition techniques. As shown and described in relation to FIGS. 1-4 and 7, a character is decomposed into a symbolic string pattern that is fed to sequential logic. Col. 3 lines 52-55 state that the class for each symbol string pattern is determined by comparing it with all sequential logics and checking which has accepted the pattern.
Sinha, R. M. K. and Prasada, B., "Visual Text Recognition through Contextual Processing," Pattern Recognition, Vol. 21, No. 5, 1988, pp. 463-479, describe visual text recognition techniques. Dictionary methods are described beginning in the last paragraph on page 463. Section 2.1 on page 465 describes a partial dictionary. Section 2.6 on page 467 describes word boundary identification based on unambiguous punctuation marks. Section 2.7 on pages 467-468 describes heuristics use to identify word boundaries, match with the dictionary, and traverse through a modified Viterbi net. Section 3 on page 468 and FIGS. 3 and 4 describe dictionary organization and search, and relate specifically to a trie structure based dictionary. FIG. 3 shows a node structure, including a NEXT field, an ALT field, a character, an end of word mark, and a flag. Page 473, right hand column, notes that errors are encountered due to several causes, including ambiguous punctuation marks within a word and mapping of numbers to a dictionary word.
Srihari, S. N., Hull, J. J., and Choudhari, R., "Integrating Diverse Knowledge Sources in Text Recognition," ACM Transactions on Office Information Systems, Vol. 1, No. 1, January 1983, pp. 68-87, describe text recognition techniques. Section 3 on pages 72-74 and FIG. 2 describe lexical organization, relating specifically to a letter trie. FIG. 2(a) shows the fields of a node, including: a token, CHAR; a word-length indicator array of bits, WL; an end-of-word tag bit, E; and two pointers labeled NEXT and ALTERNATE.